Biological pattern information processing device, biological pattern information processing method, and program

ABSTRACT

A biological pattern information processing device includes: a biological pattern information acquisition unit that acquires biological pattern information indicating a biological pattern; and a singular region detection unit that detects a singular region including damage, from the biological pattern indicated by the acquired biological pattern information.

CROSS REFERENCE TO RELATED APPLICATIONS

This application is a continuation of U.S. application Ser. No.17/364,164, filed Jun. 30, 2021, which is a continuation of U.S.application Ser. No. 15/563,211, filed Sep. 29, 2017, which issued asU.S. Pat. No. 11,055,511, which is a National Stage of InternationalApplication No. PCT/JP2016/060336 filed Mar. 30, 2016, claiming prioritybased on Japanese Patent Application No. 2015-074479 filed Mar. 31,2015, the contents of all of which are incorporated herein by referencein their entirety.

TECHNICAL FIELD

The present invention relates to a biological pattern informationprocessing device, a biological pattern information processing method,and a program.

BACKGROUND ART

In recent years, biometric authentication has attracted attention as oneof the authentication methods for identifying individuals. A biometricpattern such as a fingerprint has a feature that it does not change evenafter years of time, and is highly reliable for authentication.Meanwhile, still there is a possibility of unauthorized acts using falsebiometric patterns such as false fingers, and techniques for preventingsuch unauthorized acts have also been developed.

For example, the technique disclosed in Patent Document 1 is a techniquefor determining a false finger with a transparent thin film attached onthe surface of a finger. Patent Document 1 discloses a technique ofclassifying an image region into a plurality of regions including atleast a skin region and a background region by using colors of pixelsincluded in a captured image, and using a characteristic of a regionthat is not classified either as the skin region or as the backgroundregion, to thereby determine whether or not a foreign object is presentaround a finger. According to this technique of Patent Document 1, aforeign object around a finger (a portion having a biological pattern)can be detected.

PRIOR ART DOCUMENTS Patent Documents

-   [Patent Document 1] International Publication No. WO 2011/058836

DISCLOSURE OF THE INVENTION Problem to be Solved by the Invention

However, in order to prevent unauthorized acts related to authenticationthat uses biometric pattern information, there are some cases whereforeign object (for example, a thin film or the like) detection alonebased on color information of pixels is insufficient.

For example, in recent years, cases have been found in which biologicalpatterns (for example, fingerprints) are damaged due to surgicaloperations, burns, or the like. In this type of cases, even if apreliminarily registered biometric pattern is damaged, an ability toperform correct verification or at least detection of the presence ofdamage is required. In addition, it is required to present the detecteddamage to the user in an easy-to-understand manner.

The present invention has been made based on the problem recognitionabove. An exemplary object of the present invention is to provide abiological pattern information processing device, a biological patterninformation processing method, and a program that are capable ofdetecting a singular region, even when a biological pattern has asingular region due to damage or the like.

Means for Solving the Problem

A biological pattern information processing device according to anexemplary aspect of the present invention includes: a biological patterninformation acquisition unit that acquires biological patterninformation indicating a biological pattern; and a singular regiondetection unit that detects a singular region including damage, from thebiological pattern indicated by the acquired biological patterninformation.

In the above-described biological pattern information processing device,the biometric pattern information may include information indicating afingerprint image, the singular region detection unit may detect adirectional singular point of a ridge line from the fingerprint image,and the singular region detection unit may detect the singular regionbased on a condition of number of the directional singular point foreach of types, or a condition of a positional relationship between thetypes of the directional singular point.

In the above-described biological pattern information processing device,the biometric pattern information may include information indicating afingerprint image, the singular region detection unit may acquire aplurality of pieces of ridge line direction information respectivelycorresponding to a plurality of portions included in the fingerprintimage, the singular region detection unit may find an evaluation valuefor each of the plurality of pieces of ridge line direction information,based on a correlation between each of the plurality of pieces of ridgeline direction information and an abnormal ridge line directionalpattern template that is preliminarily held, the evaluation valuerepresenting a degree of possession of an abnormal ridge linedirectional pattern, and the singular region detection unit may detect,as the singular region, a portion corresponding to a piece of ridgedirection information in which the evaluation value is equal to orgreater than a predetermined threshold value.

In the above-described biological pattern information processing device,the biometric pattern information may include information indicating afingerprint image, the singular region detection unit may acquire aplurality of pieces of ridge line direction information respectivelycorresponding to a plurality of regions included in the fingerprintimage, the singular region detection unit may obtain smoothed ridge linedirection information by performing a direction smoothing process foreach of the plurality of pieces of ridge line direction informationbased on ridge line direction information corresponding to a regionpositioned around the region corresponding to the ridge line directioninformation, and the singular region detection unit may detect, as thesingular region, the region corresponding to the ridge line directioninformation in which a difference between the ridge line directioninformation and the smoothed ridge line direction information is greaterthan a predetermined threshold value.

In the above-described biological pattern information processing device,the biometric pattern information may include information indicating afingerprint image, the singular region detection unit may acquire aplurality of pieces of ridge line direction information and a pluralityof pieces of ridge line pitch information respectively corresponding toa plurality of regions included in the fingerprint image, the singularregion detection unit may find an evaluation value based on theplurality of pieces of ridge line direction information and theplurality of pieces of ridge line pitch information, the evaluationvalue taking a greater value as a difference in ridge line directionsand a difference in ridge line pitches become greater between adjacentregions in the fingerprint image, and in a case where the evaluationvalue is greater than a predetermined threshold value, the singularregion detection unit may detect the adjacent regions as being thesingular region by a cutting-off process.

The above-described biological pattern information processing device mayfurther include: a collation unit that collates biological patterninformation corresponding to a region other than the detected singularregion among the acquired biological pattern information, withpre-registered biological pattern information which is biologicalpattern information registered preliminarily in association withidentification information for identifying an individual.

In the above-described biological pattern information processing device,in a case of performing the collation, the collation unit may make atleast one of a positional deviation allowance and a mismatch allowanceto be allowed to vary, the positional deviation allowance representing adegree to which positional deviation is allowed, and the mismatchallowance representing a degree to which a mismatch is allowed arevariable.

The above-described biological pattern information processing device mayfurther include: a repair unit that repairs damage in biological patterninformation corresponding to the singular region in the acquiredbiological pattern information. The collation unit performs thecollation by regarding a region corresponding to the repaired biologicalpattern information as a region other than the singular region.

In the above-described biological pattern information processing device,the biometric pattern information may be information including afingerprint image, and the repair unit may repair the damage byexcluding, from the fingerprint image, an exclusion region defined basedon the singular region.

In the above-described biological pattern information processing device,the biological pattern information may be information including afingerprint image, the repair unit may find an evaluation value for eachof a plurality of portions included in the fingerprint image, based on acorrelation between ridge line direction information and an abnormalridge line directional pattern template that is preliminarily held, theevaluation value representing a degree to which the ridge line directioninformation has an abnormal ridge line directional pattern, the repairunit may extract a linear component of the evaluation value in thefingerprint image, and the repair unit may repair the damage by mutuallyreplacing fingerprint images included in a first polygon and a secondpolygon determined based on the extracted linear component.

A biological pattern information processing method according to anexemplary aspect of the present invention includes: acquiring biologicalpattern information indicating a biological pattern; and detecting asingular region including damage, from the biological pattern indicatedby the acquired biological pattern information.

A program according to an exemplary aspect of the present inventioncauses a computer to execute: acquiring biological pattern informationindicating a biological pattern; and detecting a singular regionincluding damage, from the biological pattern indicated by the acquiredbiological pattern information.

Effect of the Invention

According to the present invention, when a biological pattern has asingular region such as damage, the singular region can be detected.This leads to being able to perform a collation process while preventingdegradation of accuracy due to the singular region.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram showing a schematic functional configurationof a biological pattern information processing device according to afirst exemplary embodiment.

FIG. 2 is a block diagram showing a schematic functional configurationinside a singular region detection unit according to the first exemplaryembodiment.

FIG. 3A is a schematic diagram showing an example of an abnormal patternin a fingerprint to be detected by the singular region detection unitaccording to the first exemplary embodiment.

FIG. 3B is a schematic diagram showing an example of an abnormal patternin a fingerprint to be detected by the singular region detection unitaccording to the first exemplary embodiment.

FIG. 3C is a schematic diagram showing an example of an abnormal patternin a fingerprint to be detected by the singular region detection unitaccording to the first exemplary embodiment.

FIG. 4A is a schematic diagram showing an example of a fingerprint imageincluding an abnormal ridge line direction to be detected by thesingular region detection unit according to the first exemplaryembodiment.

FIG. 4B is a schematic diagram showing an example of a fingerprint imageincluding an abnormal ridge line direction to be detected by thesingular region detection unit according to the first exemplaryembodiment.

FIG. 4C is a schematic diagram showing an example of a fingerprint imageincluding an abnormal ridge line direction to be detected by thesingular region detection unit according to the first exemplaryembodiment.

FIG. 5A is a conceptual diagram schematically showing a template used bythe singular region detection unit according to the first exemplaryembodiment and corresponding to an abnormal ridge line directionalpattern (X type).

FIG. 5B is a conceptual diagram schematically showing a template used bythe singular region detection unit according to the first exemplaryembodiment and corresponding to a (w type) of an abnormal ridge linedirectional pattern.

FIG. 5C is a conceptual diagram schematically showing a template used bythe singular region detection unit according to the first exemplaryembodiment and corresponding to an abnormal ridge line directionalpattern (comb type).

FIG. 6 is a schematic diagram conceptually showing an example of afingerprint image including ridge line breakage to be detected by thesingular region detection unit according to the first exemplaryembodiment.

FIG. 7 is a diagram for describing a direction smoothing processperformed inside the singular region detection unit according to thefirst exemplary embodiment, schematically showing ridge line directionimage data before the direction smoothing process.

FIG. 8 is a diagram for describing a direction smoothing processperformed inside the singular region detection unit according to thefirst exemplary embodiment, schematically showing ridge line directionimage data after the direction smoothing process.

FIG. 9 is a schematic diagram for describing a cutting process of afingerprint to be detected by the singular region detection unitaccording to the first exemplary embodiment, and is a diagram showingbefore the cutting process.

FIG. 10 is a schematic diagram for describing a cutting process of afingerprint to be detected by the singular region detection unitaccording to the first exemplary embodiment, and is a diagram showingafter the cutting process.

FIG. 11 is a block diagram showing a schematic functional configurationof a biological pattern information processing device according to asecond exemplary embodiment.

FIG. 12 is a block diagram showing a schematic functional configurationof a biological pattern information processing device according to athird exemplary embodiment.

FIG. 13 is a schematic diagram for describing Z type surgery to bedetected by the singular region detection unit according to the thirdexemplary embodiment, showing an example of a fingerprint image beforeperforming the Z type surgery.

FIG. 14 is a schematic diagram for describing Z type surgery to bedetected by the singular region detection unit according to the thirdexemplary embodiment, showing an example of a fingerprint image afterperforming the Z type surgery.

FIG. 15 is a block diagram showing a schematic functional configurationinside a repair unit according to the third exemplary embodiment.

FIG. 16 is a schematic diagram showing a method of performing arestoration process based on an image of a Z type surgery fingerprint bymeans of the repair unit according to the third exemplary embodiment.

FIG. 17 is a block diagram showing a schematic functional configurationof a repair unit according to a modification of the third exemplaryembodiment.

EMBODIMENTS FOR CARRYING OUT THE INVENTION

Next, several exemplary embodiments of the present invention will bedescribed with reference to the drawings.

First Exemplary Embodiment

FIG. 1 is a block diagram showing a schematic functional configurationof a biological pattern information processing device according to afirst exemplary embodiment. As shown in FIG. 1 , a biological patterninformation processing device 1 according to the present exemplaryembodiment includes a biological pattern information acquisition unit 11and a singular region detection unit 61. The biological patterninformation acquisition unit 11 may be hereinafter referred to as aninformation acquisition unit 11 in some cases.

The information acquisition unit 11 acquires biological patterninformation from the outside.

The singular region detection unit 61 performs a process of detecting asingular region based on the biological pattern information acquired bythe information acquisition unit 11. Then, the singular region detectionunit 61 outputs information indicating whether a singular region isdetected (presence or absence of a singular region) and information onthe position of the singular region when a singular region is detected(position information indicating a range of the singular region).Details of the determination process performed by the singular regiondetection unit 61 will be described later.

The singular region is a region where singular biological patterninformation due to a part of the living body being damaged is present (adamaged part) or a region where the biological pattern information isdistorted due to wrinkles or the like on the surface of the living body.In the singular region, there is a possibility that a pattern differentfrom the pattern originally possessed by the living body is present.Examples of the cause of living body damage causing a singular region tooccur include cuts, scratches, etc., burns, and burn sores due tochemicals (for example, strong acids, etc.).

A typical example of the biological pattern information handled by thebiological pattern information processing device 1 is fingerprintinformation. A fingerprint is a pattern formed by ridge lines on thesurface of a finger or a toe. Human skin is formed by overlapping ofepidermis and dermis. The epidermis is present on the surface side(outside), and the dermis is present on the far side (inside). A layercalled a papillary layer exists in the portion where the epidermis andthe dermis are in contact with each other. In the vicinity of thispapillary layer, concaves and convexes are present on the dermis side,and this convex part forms ridge lines. Sweat gland pores are alignedalong the convex part of the ridge line. The ridge line pattern formedon the dermis side can also be seen as it is on the epidermis side. Thispattern is generally called a fingerprint. Even if the epidermis isdamaged, as long as the ridge line structure in the dermis ismaintained, when the epidermis is regenerated, a pattern based on theridge line structure of the original dermis side is reproduced also onthe epidermis side. In this case, the biological pattern informationprocessing device 1 treats fingerprint image information obtained as atwo-dimensional image as biological pattern information.

Another example of the biological pattern information is not thefingerprint itself but an arrangement pattern of blood capillaries andsweat gland pores near the surface of the finger.

When the biological pattern information to be handled is limited to afingerprint image, the biological pattern information processing devicemay be called a “fingerprint processing device”.

Specific methods for acquiring fingerprints vary as follows. It ispossible to select them appropriately from among these methods.

The first method of acquiring a fingerprint is a method in which theepidermis of a finger having a fingerprint is image-captured by a cameraor the like and information on the image is acquired as biologicalpattern information.

The second method of acquiring a fingerprint is a method of acquiring adistribution of electrical characteristics of the surface of a finger asbiological pattern information by using a sensor in contact with theliving body. The electrical characteristics of the surface of a fingervary from part to part depending on, for example, the shape of the ridgelines and the presence or absence of sweat gland pores, and it ispossible to acquire a two-dimensional distribution of suchcharacteristics as pattern information.

The third method of acquiring a fingerprint is a method of transferringa substance such as ink attached to a finger to a medium such as paperand reading the image obtained on the medium with an optical scanner orthe like.

The fourth method of acquiring a fingerprint is a method of acquiring apattern of the surface of a finger (living body) by using a technique ofOCT (optical coherency tomography). The OCT is a method of acquiringmutual interference caused by the phase difference between lightreflected from an observation target as a result of shining it on theobservation target and a reference light, as a pattern image of lightintensity. In the case of using the OCT, by appropriately changing thewavelength of light, not only information on the surface of a finger butalso information of a pattern inside of the living body at certaindepths (depths of approximately several hundreds of micrometers to 2,000micrometers) can be acquired. In this way, not only the surface but alsoa pattern inside the living body can be used as biological patterninformation. Examples of information on patterns inside a living bodythat can be used include ridge line patterns in the dermis, arrangementpatterns of the sweat gland pores, and arrangement patterns of the bloodcapillaries. When acquiring information of a pattern inside a livingbody using the OCT, for example, it is used as information of atwo-dimensional image for each cross section at a predetermined depth.Alternatively, it is also possible to use three-dimensional biologicalpattern information obtained by superimposing a number oftwo-dimensional images from each of these cross sections.

When three-dimensional information is not included, fingerprintinformation is represented as a two-dimensional image. Also, when onlyinformation about a certain layer is extracted from three-dimensionalinformation, that information can be expressed as a two-dimensionalimage. Hereinafter, these two-dimensional images may be referred to as“fingerprint images” in some cases.

(Regarding Method of Singular Region Detection Processing)

In the following, an internal configuration of the singular regiondetection unit 61 and a method of singular region detection processingwill be described. Here, target biological pattern information isinformation of a fingerprint image.

FIG. 2 is a block diagram showing a schematic functional configurationinside the singular region detection unit 61. As shown in FIG. 2 , thesingular region detection unit 61 includes a common function group, anabnormal pattern detection function, an abnormal ridge line directiondetection function, a ridge line breakage detection function, and acutout work detection function. The common function group includes aridge line direction detection unit 70, a ridge line pitch detectionunit 71, a ridge line intensity detection unit 72, and a directionsingular point detection unit 73. The operation of the singular regiondetection unit 61 using these functions will be described below.

The singular region detection unit 61 first receives data of afingerprint image from the singular region detection result acquisitionunit 12.

The singular region detection unit 61 analyzes the received fingerprintimage using functions included in the common function group thereof.Specifically, the ridge line direction detection unit 70 detects a ridgeline direction in the fingerprint image. Further, the ridge line pitchdetection unit 71 detects a ridge line pitch in the fingerprint image.Moreover, the ridge line intensity detection unit 72 detects a ridgeline intensity in the fingerprint image. In addition, the directionsingular point detection unit 73 detects a directional singular point inthe fingerprint image. The singular region detection unit 61 may detectonly one of a ridge line direction, a ridge line pitch, a ridge lineintensity, and a direction singularity, but not all of them. The processitself of detecting these ridge line pitch, ridge line intensity, anddirectional singular point from the fingerprint image is a featureextraction process in a general fingerprint authentication technique andcan be performed using existing techniques.

A ridge line direction is a direction in which the ridge is oriented. Aridge line pitch is a width of parallel ridge lines (a distance from oneridge line to another parallel adjacent ridge line). A ridge lineintensity is a degree indicating the likelihood of being a ridge line asinformation obtained from an image. A directional singular point is aportion where a ridge line direction becomes discontinuous in afingerprint image.

The singular region detection unit 61 first extracts a ridge linedirection, a ridge line pitch, and a ridge line intensity from thereceived fingerprint image using Gabor filters. Specifically, thesingular region detection unit 61 applies the Gabor filters in which thedirection and pitch are stepwise changed for each pixel included in thefingerprint image, and the direction and the pitch of the filter thatyields the highest absolute value among these applied Gabor filters isregarded as the ridge line direction and the pitch at that pixel. Inaddition, the singular region detection unit 61 extracts the absolutevalue of the filter applied value at that time as the ridge lineintensity.

Further, the singular region detection unit 61 detects a directionalsingular point in the fingerprint image. At a directional singular pointthere exists a directional shape called a delta and a directional shapecalled a core. Of these, the core can be further classified into a truecircular core and a semicircular core. A true circular core is a corewhose ridge line rotates 360 degrees around the directional singularpoint. A semicircular core is a core whose ridge line rotates 180degrees around the directional singular point. As a method of detectinga directional singular point, the singular region detection unit 61 usesan existing technique. As an example, a method for detecting adirectional singular point is also disclosed in the literature [AskerMichel Bazen, “Fingerprint identification: Feature Extraction, Matching,and Database Search”, Twente University Press, 2002]. For each finger,the singular region detection unit 61 stores the number of each of thedetected true circular cores, semicircular cores, and deltas and theposition (coordinates) of the directional singular point of each ofthese, for processing in a later step. Moreover, the singular regiondetection unit 61 detects the direction of the pattern at thedirectional singular point (for example, in the case of a semicircularcore, whether the side on which the ridge line is open is the upper sideor the lower side of the finger), and stores the information forprocessing in a later step.

In addition to the example of the existing technique mentioned above,the singular region detection unit 61 may use another method. In orderto improve the accuracy, the singular region detection unit 61 may alsouse in combination another means for correcting extraction errors inridge line direction and ridge line pitch.

Next, the singular region detection unit 61 performs processing fordetecting four types of singular regions in the fingerprint image. Thefour types are (1) abnormal pattern, (2) abnormal ridge line direction,(3) ridge line breakage, and (4) cutout work. The features of thefingerprints having these four types of abnormality and the detectionmethods thereof will be described below.

((1) Abnormal Pattern Detection)

The singular region detection unit 61 includes an abnormal patterndetection unit 74 as a function for detecting an abnormal pattern. Theabnormal pattern detection unit 74 detects an abnormal pattern based onthe number and positional relationship of the directional singularpoints (delta, semicircular core, perfect circle core) detected above.Normal fingerprint images are classified into four types of patternsfrom the ridge line direction patterns. The four types are arch-shapedpattern, loop-shaped pattern, spiral-shaped pattern, and variant-shapedpattern. For each of these patterns, the number and positionalrelationship of the directional singular points are defined.

Specifically, in the arch-shaped pattern, the number of cores is 0 andthe number of deltas is also 0. That is to say, the curve of the ridgeline is smooth. In the loop-shaped pattern, the number of semicircularcores is 1, and the number of deltas is 1 or less. In the spiral-shapedpattern, either the number of circular cores is 1 and the number ofdeltas is 2 or less, or the number of semicircular cores is 2 and thenumber of deltas is 2 or less. In the variant-shaped pattern, either thenumber of semicircular cores is 3 and the number of deltas is 3 or less,or the number of circular cores is 1, the number of semicircular coresis 1, and the number of deltas is 3 or less. In the case of a normalfingerprint image, the positional relationship of the directionalsingular point also has a predetermined restriction.

Normal fingerprint images have the above patterns. The abnormal patterndetection unit 74 detects an abnormal pattern image that cannot appearin a normal fingerprint image as an abnormal pattern. Specifically, thesingular region detection unit 61 detects the fingerprint image as anabnormal pattern when any one of the following conditions (A) to (F) issatisfied.

Condition (A): When there are two or more circular cores

Condition (B): When there are four or more semicircular cores

Condition (C): When two or more semicircular cores are present and oneor more circular cores are also present

Condition (D): When there are four or more deltas

Condition (E): Delta is present above the core (on the side near thefingertip)

Condition (F): When there are two or more semicircular cores on theupper side

That is to say, the singular region detection unit 61 detects thedirectional singular point of the ridge line included in the fingerprintimage, and detects a singular region based on the condition of thenumber of each directional singular point type or the condition of thepositional relationship between the types of the directional singularpoints.

One of the reasons why these types of abnormal patterns are detected infingerprints is a surgical treatment applied to the finger.

FIG. 3A to FIG. 3C show examples of abnormal patterns in fingerprints.In FIG. 3A to FIG. 3C, the circled portions are circular cores. Also,the portions indicated by triangles are deltas. The example of thefingerprint image shown in FIG. 3A has two circular cores and fourdeltas. That is to say, this fingerprint image meets the aboveconditions (A) and (D), and the abnormal pattern detection unit 74determines that it has an abnormal pattern. The example of thefingerprint image shown in FIG. 3B has two circular cores and fourdeltas. Also, in the example of FIG. 3B, a delta exists above the twocircular cores. That is to say, this fingerprint image meets the aboveconditions (A), (D), and (E), and the abnormal pattern detection unit 74determines that it has an abnormal pattern. The example of thefingerprint image shown in FIG. 3C has two circular cores. That is tosay, this fingerprint image meets the above condition (A), and theabnormal pattern detection unit 74 determines that it has an abnormalpattern.

If an abnormal pattern is detected, the abnormal pattern detection unit74 outputs the type of the abnormality (any of the conditions (A) to (F)above) and the position and type of the directional singular pointconcerning the abnormality.

Further, if an abnormal pattern is not detected, the abnormal patterndetection unit 74 outputs information indicating that an abnormalpattern is not detected.

((2) Abnormal Ridge Line Direction Detection)

The singular region detection unit 61 detects an abnormal pattern in theridge line direction. There are several patterns also in abnormal ridgeline directions. Typical three types of patterns are called comb typedirection pattern, ω type direction pattern, and X type directionpattern for convenience. In the present exemplary embodiment, thesingular region detection unit 61 detects three types of abnormal ridgeline directions, namely the comb type direction pattern, the ω typedirection pattern, and the X type direction pattern. There is apossibility that these types of abnormal ridge line direction patternsmay be observed at a boundary portion of the transplanted epidermis partif a fingerprint epidermis transplant operation or the like has beenperformed. These patterns cannot be seen in normal fingerprint images.

FIG. 4A to FIG. 4C show examples of fingerprint images includingabnormal ridge line directions.

FIG. 4A is an example of a fingerprint image having an abnormal ridgeline direction called a comb type direction pattern. This comb typeabnormal ridge line direction is a ridge line direction pattern which islikely to occur near the boundary of the transplanted epidermis when thefingerprint epidermis is cut and removed with a scalpel, and a surgicaloperation such as changing the position and replacement is performed.

FIG. 4B is an example of a fingerprint image having an abnormal ridgeline direction called a ω type direction pattern. This ω type abnormalridge line direction is also a ridge line direction pattern which islikely to occur near the boundary of the transplanted epidermis when thefingerprint epidermis is cut and removed with a scalpel, and a surgicaloperation such as s changing the position and replacement is performed.In addition, the ω type direction pattern is a pattern which is likelyto occur also when a deep cut is made to the arch-shaped portion of thefingerprint with a blade or the like.

FIG. 4C is an example of a fingerprint image having an abnormal ridgeline direction called an X type direction pattern. This abnormal ridgeline direction of the X type is a ridge line direction pattern which islikely to occur in the stitched portion when the skin is tightlystitched with a surgical thread or the like.

FIG. 5A to FIG. 5C are conceptual diagrams schematically showingtemplates corresponding to each of the abnormal ridge line directionpatterns (comb type, ω type, and X type).

FIG. 5A corresponds to the X type direction pattern in the fingerprintimage.

FIG. 5B corresponds to the ω type direction pattern in the fingerprintimage.

FIG. 5C corresponds to the comb type direction pattern in thefingerprint image.

The singular region detection unit 61 includes, in the interior thereof,a comb type direction pattern detection unit 75, a ω type directionpattern detection unit 76, and an X type direction pattern detectionunit 77 in correspondence with the abnormal direction patterns describedabove. The singular region detection unit 61 performs processing fordetecting these abnormal direction patterns by using information on theridge line direction and information on the ridge line intensity alreadydetected by the above method.

A processing method for detecting each of the abnormal directionpatterns will be described below.

The comb type direction pattern detection unit 75 calculates and outputsthe degree indicating the likelihood of the comb type direction patternof the fingerprint image using the data input of the ridge linedirection and the ridge line intensity preliminarily detected based onthe given fingerprint image.

Specifically, the comb type direction pattern detection unit 75preliminarily holds comb type template data representing the directionpattern as shown in FIG. 5C in the internal memory. The comb typetemplate data is data obtained on the basis of a fingerprint imagehaving the comb type direction pattern, and is stored in a twodimensional array corresponding to polar coordinates. “i” which is thefirst index of comb template data Tk (i, j) which is a two dimensionalarray, corresponds to the displacement angle around the center of thetemplate data. “i=1, 2, . . . , M” is satisfied. This “i” is an indexvalue corresponding to each direction when 360 degrees in all directionsfrom the center of the fingerprint image are cut in increments of(360/M) degrees. Where the positive direction of the x-axis of the xyorthogonal coordinate system is 0 degrees, the counterclockwisedirection is the positive direction of the displacement angle. Moreover,a second index “j” corresponds to the distance from the center of thefingerprint image. “j=1, 2, . . . , N” is satisfied. This “j” is anindex value corresponding to the distance from the center of thetemplate. The value of each element of Tk (i, j) is a two dimensionalvector (unit vector) value representing the ridge line direction in theportion (small region) represented by this polar coordinate.

The comb type direction pattern detection unit 75 calculates the maximumvalue of the sum of the inner products of the template direction Tk (k,t) and the direction vectors in the ridge line direction within thecircle of the image while changing the template rotation angle t, withrespect to an arbitrary pixel position (x, y) in the given fingerprintimage. The maximum value is expressed by Ek (x, y) according to thefollowing equation.

$\begin{matrix} {{E{k( {x,y} )}} = {\max\limits_{{t = 1},\ldots,M}( {\sum\limits_{i = 1}^{N}\frac{T{{k( {t,i} )} \cdot {{Id}( {{x + {d{x(i)}}},{y + {d{y(i)}}}} )}}}{N}} }} \} & ( {{Equation}1} )\end{matrix}$

In Equation (1) above, Id (x, y) is a unit vector representing the ridgeline direction at a coordinates (x, y) of the fingerprint image. Tk (t,i) is the i-th direction of the comb template (rotation angle is t). dx(i) is the x coordinate displacement of the i-th element in thetemplate. dy (i) is the y coordinate displacement of the i-th element inthe template.

That is to say, the value of Ek (x, y) calculated by this Equation (1)is a correlation value when correlation between the fingerprint imageand the template is the greatest (when t corresponds to such an angle)where the template is rotated 360 degrees at the coordinates (x, y) ofthe fingerprint image.

Also, at this time, the ridge line direction (displacement angle) isexpressed as a numerical value in the range up to 180 degrees in thecounterclockwise direction where the X axis positive direction is takenas 0 degree. However, since the 0 degree direction and the 180 degreesdirection need to be regarded as substantially the same direction, theinner product is yielded upon converting the angle of the directionvector so that the angle formed thereby with the X axis positivedirection (0 degree direction) is doubled.

The value of Ek (x, y) calculated by the Equation (1) above is an indexrepresenting the directional consistency between the fingerprint imageand the template. Further, the comb type direction pattern detectionunit 75 calculates a comb type evaluation value Wk (x, y) multiplied bythe ridge line intensity. The ridge line intensity representsfingerprint likeness.

Wk(x,y)=max(0,Ek(x,y)−C)×Is(x,y)  (Equation 2)

In the above equation, C is an appropriately set threshold value. Thatis to say, the threshold value C has an effect of removing a portionwhere the value of Ek (x, y) is equal to or less than C as noise. Is (x,y) is an average value of the ridge line intensity within the sameradius as the template where the coordinates (x, y) serves as thecenter.

That is to say, the evaluation value Wk (x, y) is obtained bysubtracting the threshold value C from the value of Ek (x, y) (in thecase where the result becomes negative, it is set to 0) and furthermultiplying by the ridge line intensity in the vicinity of thecoordinate (x, y).

The comb type direction pattern detection unit 75 outputs thiscalculated value Wk (x, y) as a comb type abnormality degree. This combtype abnormality degree is a degree indicating the likelihood of a combtype direction pattern.

The ω type direction pattern detection unit 76 preliminarily holds ωtype template data representing the direction pattern as shown in FIG.5B in the internal memory. The data structure of the ω type templatedata itself is similar to that of the comb type template data. The ωtype template data is template data representing a ridge line direction,and is preliminarily created based on an actual fingerprint image havinga ω type direction pattern. Based on the given fingerprint image and theω type template data above, the ω type direction pattern detection unit76 calculates a ω type abnormality degree Wo (x, y) by using the sameprocedure as that of the above calculation procedure of the comb typedirection pattern detection unit 75.

The X type direction pattern detection unit 77 preliminarily holds Xtype template data representing the direction pattern as shown in FIG.5A in the internal memory. The data structure of the X type templatedata itself is similar to that of the comb type template data. The Xtype template data is template data representing a ridge line direction,and is preliminarily created based on an actual fingerprint image havingan X type direction pattern. Based on the given fingerprint image andthe X type template data above, the X type direction pattern detectionunit 77 calculates an X type abnormality degree Wx (x, y) by using thesame procedure as that of the above calculation procedure of the combtype direction pattern detection unit 75.

The singular region detection unit 61 determines whether or not it is anabnormal ridge line direction fingerprint based on whether or not eachmaximum value of the comb type abnormality degree Wk (x, y) output fromthe comb type direction pattern detection unit 75, the ω typeabnormality degree Wo (x, y) output from the ω type direction patterndetection unit 76, and the X type abnormality degree Wx (x, y) outputfrom the X type direction pattern detection unit 77, exceeds apredetermined threshold. If the value exceeds the threshold value, thesingular region detection unit 61 determines that the fingerprint imageis an abnormal ridge line direction fingerprint (that is to say, a combtype direction pattern, a ω type direction pattern, or an X typedirection pattern). In other cases, it is determined that it is not anabnormal ridge line direction fingerprint.

As another method, the singular region detection unit 61 may determinewhether or not it is an abnormal ridge line direction fingerprint basedon whether or not the sum of each maximum value of the comb typeabnormality degree Wk (x, y) output from the comb type direction patterndetection unit 75, the ω type abnormality degree Wo (x, y) output fromthe ω type direction pattern detection unit 76, and the X typeabnormality degree Wx (x, y) output from the X type direction patterndetection unit 77, exceeds a predetermined threshold. When the valueexceeds the threshold value, the singular region detection unit 61determines that the fingerprint image is an abnormal ridge linedirection fingerprint. The singular region detection unit 61 determinesthat it is not an abnormal ridge line direction fingerprint in othercases.

That is to say, the singular region detection unit 61 acquires ridgeline direction information for each portion included in the fingerprintimage. Moreover, the singular region detection unit 61 finds anevaluation value indicating the extent to which the ridge line directioninformation has an abnormal ridge line direction pattern, based on thecorrelation between the ridge line direction information, and thetemplate of the abnormal ridge line direction pattern held preliminary(such as the comb type direction pattern, the ω type direction pattern,and the X type direction pattern). Further, when the evaluation value isequal to or greater than the predetermined threshold value, the singularregion detection unit 61 detects a portion corresponding to the ridgeline direction information as a singular region.

When it is determined that it is an abnormal ridge line directionfingerprint, the singular region detection unit 61 outputs theinformation on the position of the singular region.

Weighting may be performed with a probability distribution of eachevaluation value (comb type abnormality degree, ω type abnormalitydegree, and X type abnormality degree) based on an actual fingerprintdatabase. Thereby, the determination accuracy of the singular regiondetection unit 61 can be further enhanced.

In the present exemplary embodiment, the singular region detection unit61 includes the comb type direction pattern detection unit 75, the ωtype direction pattern detection unit 76, and the X type directionpattern detection unit 77 in the interior thereof, and detects theabnormal ridge line direction corresponding thereto. However, it is notlimited to this type of configuration. The configuration may omit someof these.

Conversely, there may be provided templates of other direction patternsto detect abnormal ridge line directions other than these three types.As an example, it is possible to adopt a configuration capable ofdetecting a pattern slightly changing the ridge line angle of the combtype, the ω type, and the X type, or a configuration capable ofdetecting several types of patterns as a result of changing the radiusof the template.

The above method of detecting a singular region based on the number ofdirectional singular points and the positional relationship betweendirectional singular points, described as “(1) Abnormal PatternDetection”, is an effective technique when a clear image of an entirefingerprint is obtained. On the other hand, in the present method ((2)Abnormal Ridge Line Direction Detection) that uses evaluation valuesbased on a template, there is an advantage that it enables detection ofan abnormal fingerprint of a specific shape even when only an image ofonly a fraction of the fingerprint is acquired.

((3) Ridge Line Breakage Detection)

The singular region detection unit 61 also has a function of detectingbreakage of a ridge line in the fingerprint. Specifically, the singularregion detection unit 61 includes, in the interior thereof, a ridge linebreakage detection unit 78. The singular region detection unit 61performs processing for detecting these abnormal direction patterns byusing information on the ridge line direction and information on theridge line intensity already detected by the above method.

FIG. 6 is an example conceptually showing a fingerprint image includingridge line breakage. In the example of the fingerprint image shown inFIG. 6 , a part of the ridge lines which were originally continuous isdiscontinuous, and there is a dotted pattern in that part. In FIG. 6 , aportion indicated by an elliptical frame is a portion that includesridge line breakage. This type of ridge line breakage can be caused byburns or chemical damage.

The ridge line breakage detection unit 78 acquires the data of the ridgeline direction image acquired by the above-described method. This ridgeline direction image data includes ridge line direction data at eachpixel. Then, the ridge line breakage detection unit 78 executes adirection smoothing process over a large area. This direction smoothingprocess is a process of correcting a portion in the fingerprint imageincluding the ridge line direction detected in error due to noise or thelike, to the correct ridge line direction. The direction smoothingprocessing itself can be realized by statistical processing on pixelvalues of ridge line direction image data. The direction smoothingprocess is, for example, a process of taking a mode value of a directioncomponent of a region within a certain range, or an average of directionvectors of a region within a certain range.

FIG. 7 and FIG. 8 are schematic diagrams for illustrating examples ofthe direction smoothing processing mentioned above. FIG. 7 shows forexample ridge line direction image data obtained for the example for thefingerprint image in FIG. 6 . In FIG. 6 , the ridge line direction isindefinite with respect to the portion where the ridge line is broken.The ridge line direction image data for this type of portion issusceptible to noise. This is because the direction of the ridge line tobe detected is not stable. Therefore, in the central portion in FIG. 7 ,the ridge line direction is not constant but random. FIG. 8 is an imageobtained as a result of the direction smoothing process performed on theridge line direction image in FIG. 7 . By executing the directionsmoothing process by means of the ridge line breakage detection unit 78over a large area, it is possible to obtain a smoothly changingdirection image shown in FIG. 8 .

Then, the ridge line breakage detection unit 78 obtains an angulardifference between the initial ridge line direction and thepost-smoothing ridge line direction, for each portion in the directionimage. When the angle (direction) has changed by a predetermined amountor more, that is, when the absolute value of the angular difference isequal to or larger than the predetermined amount, the ridge linebreakage detection unit 78 extracts this portion as a ridge linebreakage candidate region. Portions other than ridge line breakage marksdue to burns or chemicals may be extracted as ridge line breakagecandidate regions in some cases. For example, wrinkles and scars ofelderly people or the like fall under this category. Such wrinkles andscars have thin line shapes, unlike burns and traces of chemicals.Therefore, the ridge line breakage detection unit 78 repeatedly performsimage processing called expansion and contraction for the ridge linebreakage candidate region extracted above, and removes such linear (ordot) fine traces.

Then, the ridge line breakage detection unit 78 finally calculates thetotal sum of the ridge line intensity already obtained by theabove-described processing for this ridge line breakage candidateregion, thereby calculating and outputting an evaluation value for theridge line breakage detection. Then, when the evaluation value of theridge line breakage output by the ridge line breakage detection unit 78is equal to or greater than a predetermined threshold value, thesingular region detection unit 61 determines the fingerprint as having aridge line breakage trace therein. In other cases, the singular regiondetection unit 61 determines the fingerprint as being a fingerprinthaving no ridge line breakage trace.

That is to say, the singular region detection unit 61 acquires ridgeline direction information for each portion included in the fingerprintimage. In addition, the singular region detection unit 61 obtainssmoothed ridge line direction information by performing directionsmoothing processing based on ridge line direction information of aportion around each portion of the ridge line direction information.Furthermore, when (the absolute value of) the difference between theridge line direction information and the smoothed ridge line directioninformation is greater than a predetermined threshold value, thesingular region detection unit 61 detects a region corresponding to thatportion as the singular region.

When it is determined as being a fingerprint having a ridge linebreakage trace therein, the singular region detection unit 61 outputsthe information on the position of the singular region.

Among ridge line breakages, there are not only the cases of burns andchemicals, but also cases of ridge line breakage due to years of agingdeterioration and engaging in manual labor which abuses the hands. Inthe case of this type of natural breakage, not only a specific part butalso the entire fingerprint ridge line is broken. In order todistinguish between this type of natural breakage of the entire ridgeline and partial breakage (including intentional breakage) due to burnsand chemicals, the singular region detection unit 61 may determinewhether or not a portion of the fingerprint other than the ridge linebreakage candidate region has a high-quality ridge line image. As aresult, it becomes possible to detect only ridge line breakage due to aspecific condition.

The singular region detection unit 61 may also determine whether or nota ridge line breakage trace is present in the central portion of thefingerprint. Since the fingerprint central portion has a significantinfluence on the determination of the fingerprint collation, ridge linebreakage may be intentionally made in some cases. As a result, it alsobecomes possible to detect only ridge line breakage in a specificlocation.

((4) Cutout Work Detection)

The singular region detection unit 61 also has a function of detectingcutout work processing of the fingerprint. Specifically, the singularregion detection unit 61 includes a cutout work detection unit 79. Aswill be described below, the cutout work detection unit 79 determinesthe presence or absence of cutout work processing with respect to theinput fingerprint image, based on the change in the ridge line pitch(ridge line interval). This is because, in the case of a fingerprintwith cutout work having been surgically done thereto, since the skinaround the surgical operation mark is pulled while being sutured, thepitch of a specific portion of the ridge line and the pitch of the ridgeline in a specific direction locally change.

FIG. 9 and FIG. 10 are schematic diagrams for explaining the cutout workprocessing of fingerprints. FIG. 9 shows an example of a fingerprintimage before the cutout work is performed. Further, FIG. 10 shows anexample of a fingerprint image after the cutout work has been performedon the fingerprint shown in FIG. 9 . An example of cutout work bysurgery is a method such that a scalpel is placed in the diamond-shapedportion shown in the center of the fingerprint in FIG. 9 , the epidermisis cut out thereinside, and while pulling the skin in the lateraldirection, that is, in the left-right direction in a manner of closingthe cut-out diamond shape, the center part of the original diamond shapeis sutured.

In the case of performing a cutout work operation to achieve deformationon a fingerprint shown in FIG. 10 , the ridge line, on the left handside of FIG. 10 , in the direction orthogonal to the cutout direction(that is, the ridge line running in the left-right direction in thepresent example. The portion indicated by “A” in FIG. 10 ) does notreceive any change as a result of pulling. On the other hand, the ridgeline, on the right hand side of FIG. 10 , in the direction parallel tothe cutout direction (that is, the ridge running in the verticaldirection in this example. The portion indicated by “B” in FIG. 10 ) isobserved with a characteristic in which the ridge line pitch spreadsmore than the original pitch.

A method of detection processing performed by the cutout work detectionunit 79 is as follows.

The cutout work detection unit 79 first detects the scar position onwhich the cutout work has been performed. Specifically, a line segmentof a certain length at an arbitrary angle from an arbitrary pixel in theimage is generated, and the ridge line direction difference and theridge line pitch difference are added for the image portion within acertain distance range (1 to 16 pixels) from line segments on both sidesof the line segment. The coordinates (x, y) and angle (t) at which thesum value is a maximum are taken as a candidate for the scar position.

Next, the cutout work detection unit 79 calculates two types of cutoutwork evaluation values for rectangular regions (region R1 and region R2,respectively) of a predetermined size on both sides of the scar. Thefirst evaluation value Wc1 is an index for checking whether or not theridge line pitch in the same direction as the scar is widening. Thesecond evaluation value Wc2 is an index for checking whether or not theridge line pitches are different on both sides of the scar. The cutoutwork detection unit 79 calculates Wc1 and Wc2 by the followingequations.

$\begin{matrix}\begin{matrix}{{{Wc}1} = {\max\limits_{{R = {R1}},{R2}}\{ \frac{\begin{pmatrix}{90 - {{Angle}{difference}{between}{average}}} \\{{direction}{within}R{and}t}\end{pmatrix}}{90} }} \\ {\times \max( {0,\frac{{{Average}{pitch}{within}{}R} - {{Fingerprint}{average}{pitch}}}{{Fingerprint}{average}{pitch}}} ) \times {Average}{intensity}{wihtin}R} \}\end{matrix} & ( {{Equation}3} )\end{matrix}$ $\begin{matrix}{{{Wc}2} = {\frac{❘{{{Average}{pitch}{within}{}R1} - {{Average}{pitch}{within}{}R2}}❘}{{Fingerprint}{average}{pitch}} \times \min( {{{Average}{intensity}{within}{}R1},{{Average}{intensity}{within}{}R2}} )}} & ( {{Equation}4} )\end{matrix}$

That is to say, for each of the regions R1 and R2, when a product of adegree to which the ridge line direction in the region coincides with“t”, a degree to which the ridge line pitch in the region is wider thanthe ridge line pitch of the entire fingerprint (0 when it is narrowerthan the ridge line pitch of the entire fingerprint), and the ridge lineintensity in the region is obtained, the greater value is the evaluationvalue Wc1.

Further, the evaluation value Wc2 is the product of the degree of thedifference between the ridge line pitches in the regions R1 and R2 andthe ridge line intensity (the smaller one of the regions R1 and R2).

The average direction in the above equation is calculated by taking aweighted average with respect to the direction data generated by theridge line direction detection unit 70 using weighting based on theridge line intensity generated by the ridge line intensity detectionunit 72. The average pitch in the above equation is calculated by takinga weighted average with respect to the pitch data generated by the ridgeline pitch detection unit 71 using weighting based on the ridge lineintensity generated by the ridge line intensity detection unit 72.

The determination by means of the evaluation values Wc1 and Wc2calculated by the cutout work detection unit 79 is effective when thescar position is correctly detected. However, depending on thefingerprints, there are some cases where the position of the cutout workis unclear and the scar position cannot be clearly known. In order tocope with these types of cases, a method of detecting whether or not anunnatural broad pitch portion exists in the entire fingerprint is usedconcurrently, without using the scar position detected by the cutoutwork detection unit 79. Therefore, the following evaluation values Wc3and Wc4 are used as indices. The evaluation value Wc3 is an index forseeing whether or not an abnormal wide pitch portion exists. Theevaluation value Wc4 is an index for checking whether or not the pitchin a specific direction is widening. The cutout work detection unit 79calculates Wc3 and Wc4 by the following equations.

$\begin{matrix}{{{Wc}3} = \frac{\begin{matrix}{{Total}{sum}{of}{ridge}{line}{intensities}{of}} \\{1.5{or}{more}{times}{fingerprint}{average}{pitch}}\end{matrix}}{( {{Total}{sum}{of}{ridge}{line}{intensities}{of}{entire}{fingerprint}} )}} & ( {{Equation}5} )\end{matrix}$ $\begin{matrix}{{{Wc}4} = \frac{\begin{matrix}{{Average}{pitch}{of}{direction}{Dm} \times} \\{{average}{intensity}{of}{direction}{Dm}}\end{matrix}}{{Average}{pitch} \times {average}{intensity}}} & ( {{Equation}6} )\end{matrix}$

In the equation of Wc4, Dm is a direction in which the average pitch isthe maximum.

That is to say, the evaluation value Wc3 represents the ratio of theportion of the entire fingerprint where the ridge line pitch is wide(based on the pitch 1.5 times the average of the entire fingerprint),and is the value of the ratio with the ridge line intensities added.

The evaluation value Wc4 represents the ratio of the width of the pitchin the direction of a specific ridge line (the direction in which theaverage pitch is the maximum) of the entire fingerprint, and is thevalue of the ratio with the ridge line intensities added.

Finally, the cutout work detection unit 79 outputs the above-describedfour types of evaluation values Wc1, Wc2, Wc3, and Wc4. Then, thesingular region detection unit 61 determines whether or not cutout workis included in the fingerprint image, depending on whether or not therespective values of the evaluation values Wc1, Wc2, Wc3, and Wc4 areequal to or greater than a predetermined threshold value. In addition,the singular region detection unit 61 multiplies each of theseevaluation values Wc1, Wc2, Wc3, and Wc4 by a predetermined weight toobtain a weighted average (weighted average), and it is determinedwhether or not cutout work is included in the fingerprint imagedepending on whether or not the weighted average is equal to or greaterthan the predetermined threshold value.

That is to say, the singular region detection unit 61 acquires ridgeline direction information as well as ridge line pitch information foreach portion included in the fingerprint image. Moreover, the singularregion detection unit 61 finds an evaluation value which takes a greatervalue as the difference in the ridge line direction and the differencein the ridge line pitch become greater between adjacent regions in thefingerprint image, based on the ridge line direction information and theridge line pitch information. Further, when the evaluation value isgreater than the predetermined threshold value, the singular regiondetection unit 61 detects that the adjacent region is a singular regiondue to cutout work.

When it is determined as being a fingerprint with cutout work donetherein, the singular region detection unit 61 outputs the informationon the position of the singular region.

Generally, in normal fingerprints, it is known that the pitch of theridge line in the horizontal direction (short side direction of thefinger) in the vicinity of the end of the lower part of the fingerprinttends to be wider than the ridge line pitch in the other portions. Basedon this, in the processing described above, the region in the lower partof the fingerprint in which the ridge line is in the horizontaldirection may be excluded from the calculation of the evaluation valuesWc1, Wc2, Wc3, and Wc4. By performing the calculation of the evaluationvalues by the cutout work detection unit 79 in this manner, it ispossible to further enhance the determination accuracy.

For example, when a person from whom a fingerprint is to be collecteddoes not wish to authenticate themselves, the fingerprint is impressedwhile being intentionally twisted in some cases. Even in such a case,there is a tendency for the specific region of the fingerprint and thepitch in the specific direction to widen by pulling as a result oftwisting. The evaluation values Wc3 and Wc4 that do not use the scarposition can also be used for the purpose of detecting a fingerprintimpressed in a state unsuitable for authentication where an action suchas twisting is applied.

With the configuration of the first exemplary embodiment describedabove, the biological pattern information processing device 1 determineswhether or not a singular region (a damaged portion or the like) existsin the acquired biological pattern, and if it exists, the positionthereof can be specified.

Second Exemplary Embodiment

Next, a second exemplary embodiment will be described. Descriptions ofmatters common to those of the first exemplary embodiment may beomitted, and the following description focuses on matters unique to thesecond exemplary embodiment.

FIG. 11 is a block diagram showing a schematic functional configurationof a biological pattern information processing device according to thesecond exemplary embodiment. As shown in FIG. 11 , a biological patterninformation processing device 5 according to the present exemplaryembodiment includes an information acquisition unit 11, a singularregion detection unit 61, and a collation unit 116.

In the present exemplary embodiment, a pre-registered biological patterninformation memory unit 62 exists as an external function of thebiological pattern information processing device 5. The function of thepre-registered biological pattern information memory unit 62 may berealized as a function of an independent device or may be realized as afunction of a part of another device. In addition, the pre-registeredbiological pattern information memory unit 62 may be realized as afunction inside the biological pattern information processing device 5.

The information acquisition unit 11 has a function similar to that inthe first exemplary embodiment.

The singular region detection unit 61 has a function similar to that inthe first exemplary embodiment.

The collation unit 116 performs processing of collating the biologicalpattern information in the region other than the singular region amongthe biological pattern information acquired by the informationacquisition unit 11, with the pre-registered biological patterninformation preliminarily registered in the pre-registered biologicalpattern information memory unit 62. The collation unit 116 acquires fromthe singular region 61 information on the presence or absence of asingular region and the position (range) of the singular region. Thecollation unit 116 is such that when performing the collationprocessing, at least either one of a positional deviation allowancewhich represents a degree to which positional deviation is allowed, anda mismatch allowance which represents a degree to which a mismatch isallowed may be variable. Further, when it is determined that there is asingular region based on the information output by the singular regiondetection unit 61, the collation unit 116 may perform adjustment so thateither or both of the allowances change in a direction in which theallowance increases (that is to say, in a direction in which it isregarded that the degree of matching is increased despite somedifferences).

A fingerprint that is damaged due to a surgical operation or an injurymaintains the characteristic amount of the original fingerprint exceptfor the portion of the surgical operation or injury (that is, singularregion). By verifying the consistency of this portion, there is apossibility that it can be collated with the fingerprint before thesurgical operation or injury. For example, in characteristic pointcollation, whether or not the same fingerprint is present is determinedby checking whether adjacent characteristic points (ridge line endpoints and/or branch points) of the fingerprint are at a certaindistance difference or a certain angular difference. In the case ofsurgically operated fingerprints, in many cases, this allowance isexceeded by shape change due to pulling at the time of suturing.

Therefore, for a fingerprint judged to be a damaged fingerprint, bymitigating the positional deviation allowance or mismatching allowanceof fingerprint characteristics at the time of collation from a standardvalue, it is possible to manufacture a device that is characterized bybeing capable of collating with the finger of the principal personbefore the damage was made thereto.

When mitigating collation allowance, there is a disadvantage that therisk of erroneously identifying a different person as the principalperson increases. However, in the operational environment in which anoperator or the like ultimately confirms whether he/she is the sameperson or not using a face photograph or the like other than thefingerprint, it is possible to reduce this type of risk ofmisidentifying another person.

The collation process itself performed by the collation unit 116 can beperformed using existing techniques. The outline of the process ofcollating a fingerprint is as follows. For the collation processing, thecollation unit 116 extracts characteristics of the input fingerprintimage. The characteristics include the ridge line directions of thefingerprint, the statistical values relating to distribution of thedirection of the fingerprint, the manner of connection of ridge lines,the number of directional singular points of the ridge line for eachtype, the mutual positional relationship of the directional singularpoints, the orientation of straight lines connecting a plurality ofdirectional singular points, and the angle formed by these straightlines. Ridge line directional singular points are directional singularpoints such as a true circular core, a semicircular core, and a delta,which will be described later. The collation unit 116 determines whetheror not the plurality of fingerprint images are the same by evaluatingthe above-described characteristics of the plurality of fingerprintimages with the proximity and/or the distance in the characteristicspace. In one example, the collation unit 116 compares thecharacteristics of the fingerprint image preliminarily registered in thedatabase against the newly input fingerprint image, and determineswhether or not both images match.

In this type of collation processing, the above-mentioned positionaldeviation allowance is for example a value that represents the degree towhich the error in the position of the characteristic point in thefingerprint image is allowed. In addition, the mismatching allowance isa value that indicates the degree to which characteristic mismatching isstill allowed while it is regarded as matching when the two fingerprintimages to be compared do not completely match. For example, themismatching allowance may be represented by a distance that isappropriately defined in the characteristic space, or may be expressedby the degree of the weight of the penalty that is given according tothe distance.

The pre-registered biological pattern information memory unit 62memorizes pre-registered biological pattern information. Thepre-registered biological pattern information memory unit 62 holds thebiological pattern information and the identification information foridentifying the individual while associating them with each other. Inaddition, the pre-registered biological pattern information memory unit62 may further hold the above identification information with personalattribute information while associating them with each other. An exampleof individual attribute information is the full name, the information onthe registered residence, and the information on the legal status of theindividual. The pre-registered biological pattern information memoryunit 62 uses, for example, a magnetic hard disk device, a semiconductormemory, or the like as a means for storing information.

With the configuration of the second exemplary embodiment describedabove, the collation unit 116 can perform the collation processing usingthe biological pattern information of the region other than the detectedsingular region.

Further, when the singular region is detected, the collation unit 116can mitigate the positional deviation and perform the collation process.

Moreover, when the singular region is detected, the collation unit 116can mitigate the mismatching allowance and perform the collationprocess.

Third Exemplary Embodiment

Next, a third exemplary embodiment will be described. Descriptions ofmatters common to those of the above exemplary embodiment may beomitted, and the following description focuses on matters unique to thepresent exemplary embodiment.

FIG. 12 is a block diagram showing a schematic functional configurationof a biological pattern information processing device according to thethird exemplary embodiment. As shown in FIG. 12 , a biological patterninformation processing device 6 according to the third exemplaryembodiment includes an information acquisition unit 11, a singularregion detection unit 61, a repair unit 214, and a collation unit 116.

Each of the information acquisition unit 11, the singular regiondetection unit 61, and the collation unit 116 has the same function aseach function in the above-described exemplary embodiment. Thebiological pattern information processing device 6 according to thepresent exemplary embodiment is characterized in that it includes arepair unit 214.

The repair unit 214 repairs damage in the biological pattern informationthat has occurred in the singular region of the biological patterninformation included in the singular region in the biological patterninformation acquired by the information acquisition unit 11.

Then, in the present exemplary embodiment, the collation unit 116regards the biological pattern information repaired by the repair unit214 as a region other than the singular region, and performs a collationprocess.

Next, details of the process performed by the repair unit 214 will bedescribed. The repair unit 214 performs the process of repairing afingerprint of a singular region caused by a surgical operation called“Z type surgery”. The Z type surgery is a surgical operation in which ascalpel is put into the fingerprint epidermis in a Z shape and the skinsof the two triangular portions created as a result of the Z-shapedincision are replaced and then sutured again. When such surgery isperformed, a positional change of the fingerprint characteristic amountoccurs, so that it is difficult or impossible to collate it as it iswith the fingerprint before the surgery.

FIG. 13 and FIG. 14 are schematic diagrams showing examples offingerprint images for explaining Z-type surgery. FIG. 13 shows an imagebefore performing the Z type surgery. Further, FIG. 14 shows an imageafter performing the Z-type surgery. By replacing and suturing the twotriangular skins created as a result of the Z-shaped incision describedabove, the fingerprint image shown in FIG. 14 has a pattern which is notnormally possible.

The fingerprint image shown in FIG. 14 is an abnormal fingerprint. Therepair unit 214 repairs the fingerprint image shown in FIG. 14 createdas a result of the Z type surgery, that is to say, performs a processfor processing the image, and performs a process for restoring it to theoriginal fingerprint image (before the surgery) in FIG. 13 .

FIG. 15 is a block diagram showing a schematic functional configurationinside the repair unit 214. As shown in FIG. 15 , the repair unit 214includes thereinside a damaged portion detection unit 91 and a Z typesurgery fingerprint restoration unit 92, in an interior thereof.

Hereinafter, a method of processing performed by the repair unit 214will be described.

The damaged portion detection unit 91 detects a portion of the trace onwhich the operation has been performed from the fingerprint image, andoutputs an abnormality degree image representing the degree ofabnormality as an image. As an example, in the abnormality degree image,the degree of abnormality is represented in grayscale in the image.

As the degree of abnormality, the damaged portion detection unit 91 usesany one of a comb type evaluation value Wk (x, y), a ω type evaluationvalue Wo (x, y), and an X type evaluation value Wx (X, y) calculated bythe singular region detection unit 61. The damaged portion detectionunit 91 may receive these various evaluation values from the singularregion detection unit 61, or the damaged portion detection unit 91itself may calculate these various evaluation values by means of asimilar method. In addition, the damaged portion detection unit 91 mayuse another evaluation value (for example, a value indicating the degreeof direction change or pitch change as described above) as the degree ofabnormality. The damaged portion detection unit 91 may use a weightedaverage value obtained by weighting these various evaluation values andtaking the average as the abnormality degree. Then, the damaged portiondetection unit 91 creates an abnormality degree image using one of theabnormality degrees described here.

The Z type surgery fingerprint restoration unit 92 receives input of twoimages of a fingerprint image and an abnormality level image created bythe damaged portion detection unit 91, and outputs a processedfingerprint restored image.

Specifically, first, the Z type surgery fingerprint restoration unit 92applies Hough transformation to the abnormality degree image. As aresult of this Hough transformation, the Z type surgery fingerprintrestoration unit 92 detects straight line components in the abnormalitydegree image. Then, the Z type surgery fingerprint restoration unit 92detects three straight line components (from the first candidate up tothe third candidate) in which the portions with high abnormality degrees(dark portions in the case where it is expressed as an abnormalitydegree grayscale image) are linearly arranged. When these three straightline components from the first candidate to the third candidate form a“Z” shape on the fingerprint, the Z type surgery fingerprint restorationunit 92 determines that the fingerprint has undergone the Z shapeprocessing.

In order to determine whether or not the three straight line componentsfrom the first candidate to the third candidate form a “Z” shape, the Ztype surgery fingerprint restoration unit 92 uses the followingconditions (1) to (3). The condition for determining a “Z” shape is thatall of the following conditions (1) to (3) are satisfied. A proviso isthat in the condition (1) to the condition (3), the three straight linecomponents that are the first candidate to the third candidate arerepresented by straight lines (line segments) A, B, and C.

Condition (1): Two straight lines A and B the orientation (angle) ofwhich are closest to each other are near parallel. Specifically, thedifference between the orientations of the straight line A and thestraight line B is within 15 degrees, and also the straight lines A andB do not intersect within the image range.

Condition (2): A straight line C other than A and B intersects each ofthe straight lines A and B in the image range at a difference oforientation (angle) of not less than 20 degrees and not more than 60degrees.

Condition (3): The average value of the pixel values of the abnormalitydegree images on the straight lines (line segments) A, B, and C is equalto or greater than a predetermined threshold value for each line segment(for all three lines).

FIG. 16 is a schematic diagram for explaining the method of restoring aZ type surgery fingerprint. When it is determined that the inputfingerprint image is a Z type surgery fingerprint, the Z type surgeryfingerprint restoration unit 92 restores the preoperative image by thefollowing method (Step (1) to Step (6)), and outputs the obtainedpreoperative image. FIG. 16 shows three straight line componentcandidates (straight lines A, B, C) in the abnormal value image detectedat the time of the above determination. Moreover, FIG. 16 shows pointsD, E, F, and G used in the restoration procedure described below.

Step (1): The point of intersection between the straight line A and thestraight line C is taken as D, and the intersection between the straightline C and the straight line B is taken as E.

Step (2): The foot of the perpendicular drawn from the intersectionpoint E onto the straight line A (the intersection between theperpendicular line and the straight line A) is taken as F.

Step (3): The foot of the perpendicular drawn from the intersectionpoint D onto the straight line B (the intersection between theperpendicular line and the straight line B) is taken as G.

Step (4): Copy the portion surrounded by the triangle FDE (firstpolygon) of the input image onto the triangle FGE of the output image bymeans of affine transformation.

Step (5): Copy the portion surrounded by the triangle DEG (secondpolygon) of the input image onto the triangle DFG of the output image bymeans of affine transformation.

Step (6): The regions other than the portions copied in the Steps (4)and (5) above are directly copied from the input image to the outputimage.

That is to say, based on the correlation between the ridge linedirection information of each portion included in the fingerprint imageand the template of the abnormal ridge line direction pattern heldpreliminarily, the repair unit 214 finds an evaluation valuerepresenting the degree of abnormal ridge line directional patternpossession in the ridge line direction information for each of theportions, and extracts the straight line component of the evaluationvalue in the fingerprint image. In addition, the repair unit 214mutually replaces the fingerprint images included in the first polygonand the second polygon defined based on these straight line components(if the shape of the polygon to be replaced differs from the shape ofthe original polygon, the shape is appropriately adjusted by means ofaffine transformation or the like) to thereby repair the damage.

Although the points F and G used in the above method are not necessarilyguaranteed to be completely identical with the cutout part in the actualsurgical operation, the characteristic amounts of the fingerprint imageson the two triangles FGE and DFG used in the above Steps (4) and (5) canbe expected to approach the position of the fingerprint before thesurgical operation. That is to say, the repairing process of the repairunit 214 increases the possibility of successful collation with thepre-registered biological pattern information in the collation unit 116.

Further, in the case of handling a surgical fingerprint with a portionthat has been clearly processed, by performing image matching (ridgeline matching) at the boundary between the line segment DF and the linesegment DE of the transformed portion, the repair unit 214 may correctthe coordinate position of the point F which is the starting point F ofthe processing. Similarly, by performing image matching at the boundarybetween the line segment EG and the line segment ED of the transformedportion, the coordinate position of the point G serving as the startingpoint of processing may be corrected.

(Modification 1 of Repair Unit)

The process of the repair unit 214 may be performed in a manner of thefollowing modification.

Here is described a case where the input fingerprint image is determinedas containing cutout work damage, based on the evaluation values Wc1 andWc2 calculated by the above-described cutout work detection unit 79. Inthis case, the repair unit 214 detects a rectangular region having awide pitch on the wide pitch side of the detected scar, calculates theproduct of the region width and the pitch change difference in therectangle, and estimates that it is the width of the cutout portion. Asa result, it is possible to restore the peripheral portion of thefingerprint outside the diamond shape by performing image transformationsuch that the diamond region at the center part of FIG. 10 is insertedas a blank portion into the detection rectangular region of the image inFIG. 11 .

(Modification 2 of Repair Unit)

As a further modification of the repair unit 214, a repair unit 214 adescribed below may be used. The repair unit 214 a according to thepresent modification does not restore the fingerprint before surgery bymeans of transformation, but excludes the portion where the fingerprinthas been processed, extracts only the portion that has not beenprocessed, and outputs the extracted result as a restored image. That isto say, the repair unit 214 a cuts out a portion which has not beenprocessed by means of surgery or the like.

FIG. 17 is a block diagram showing a schematic functional configurationof the repair unit 214 a according to present modification. As shown inFIG. 17 , the repair unit 214 a according to the present modificationincludes a damaged portion detection unit 93 and a damaged portionremoval unit 94.

An example of the function of the damaged portion detection unit 93 issimilar to the function of the damaged portion detection unit 91described above. The damaged portion detection unit 93 may furtherinclude a function of detecting an abnormal wide pitch area or afunction of detecting a ridge line damage region (a function similar tothe above-described ridge line breakage detection unit 78). As a result,it is possible to detect a damaged portion while taking the informationof the abnormality degree image into account.

Based on the abnormality degree image generated by the damaged portiondetection unit 93, the damaged portion removal unit 94 decides anexclusion region to be excluded as a damaged portion by means of any ofthe methods listed below (method (1) to method (4)). Furthermore, thedamaged portion removal unit 94 fills the exclusion region in thefingerprint image with a background color, and then outputs the image.

Method (1): A region whose degree of abnormality is equal to or greaterthan a predetermined threshold value and within 16 pixels in thevicinity thereof (this value “16” may be set to a different value) istaken as an exclusion region.

Method (2): Extracts a region where the degree of abnormality is equalto or greater than a predetermined threshold value, and treats theregion including the region inside the abnormal region as an exclusionregion by means of an image expansion/contraction process.

Method (3): A region where the degree of abnormality is equal to orgreater than a predetermined threshold is taken as an abnormal region,and a fingerprint position that is furthest away from the abnormalregion is detected. Also, a region within which the ridge line directionand the ridge line pitch continuously vary from that position (noabnormal discontinuity) within a predetermined distance is taken as aneffective region. The portion other than the effective region is takenas an exclusion region.

Method (4): A region where the degree of abnormality is equal to orgreater than a predetermined threshold is taken as an abnormal region,and a fingerprint position that is furthest away from the abnormalregion is detected. Also, the region outside the circle which is acircle centered at that position and whose radius is the distance fromthat position to the abnormal region is taken as the exclusion region.

That is to say, the repair unit 214 a repairs the damage by removing theinformation of the fingerprint image of the exclusion region determinedbased on the singular region, from the entire fingerprint image.

Whether to employ the method out of the above methods (1) to (4) can becontrolled for example by parameters given from the outside. As anothermethod, the method (4) is applied with the highest priority, and themethod (3) is applied when the region (area) of the fingerprint imagenecessary for the collation processing cannot be obtained as a resultthereof, and from thereon, the method (2) and the method (1) may beapplied in this order in a similar manner.

Although the processing by the Z type surgery fingerprint restorationunit 92 supports only damaged fingerprints caused by surgery of aspecific method, if there is no clear surgical trace, there is apossibility that the original preoperative fingerprint cannot berestored. Even in such a case, there is an advantage that it can stillbe collated with the fingerprint of the principal person beforeprocessing was done on the finger, by excluding the portion where theprocessing of the fingerprint has been performed, by means of the methodusing the damaged portion removal unit 94.

Also, the case of this modification, in terms of removing information onthe damaged portion, is an example of a case where the repair unit 214a, concerning the biological pattern information included in a singularregion among the biological pattern information acquired by theinformation acquisition unit 11, repairs the damage in the biologicalpattern information that has occurred in this singular region.

The process performed by the repair unit 214 (or a modification thereof)(the process of restoring a Z type surgery fingerprint to a state beforesurgical operation, or the process of excluding the damaged portion)does not necessarily guarantee accurate restoration of the fingerprintbefore the surgery. For example, a normal fingerprint of a fingerwithout a surgery history may be determined as having undergone asurgical operation as a result of a false determination, and there maybe some cases where it may still be processed. However, for example, bymeans of the collation unit 116 checking both the fingerprint imageprior to the processing by the repair unit 214 (or a modificationthereof) and the processed fingerprint image against the pre-registeredbiological pattern information memory unit 62 (fingerprint database), itis possible to reduce the risk of lowering the authentication rate. Whencollating both fingerprint images of before and after processing withthe pre-registered biological pattern information memory unit 62, if oneof the fingerprint images coincides with the pre-registered biologicalpattern, it can be regarded as matching with the pre-registeredbiological pattern.

Also, as a result of the processing performed by the repair unit 214 (ora modification thereof), there is also a risk that the fingerprint imageafter the restoration process coincides with the fingerprint of anotherperson. However, if the operation is performed such that an operator orthe like separately makes a confirmation using means other thanfingerprints (for example, a face photograph, etc.) instead of making afinal decision based only on that match, this type of risk inmisidentification of another person can be reduced.

According to the configuration of the third exemplary embodimentdescribed above, when the singular region is detected, the biologicalpattern information can be repaired. One method of restoration is toexclude the excluded region including the singular region from thecollation process target. Another method of restoration is to restorethe biological pattern replacement when a surgical operation or the likehas been done, and perform the collation process based on thecharacteristics after restoration to the original has been done. As aresult, accuracy in detecting a preliminarily registered specificbiological pattern is improved.

The functions of the biological pattern information processing device inthe above-described exemplary embodiments may be realized by a computer.In this case, it may be realized by recording a program for realizingthe functions of this device on a computer-readable recording medium,and by causing the computer system to read and execute the programrecorded on the recording medium. The term “computer system” referred tohere includes hardware such as an OS and peripheral devices. Moreover,the term “computer-readable recording medium” refers to a portablemedium such as a flexible disk, a magnetic optical disk, a ROM, and aCD-ROM, or a memory device such as a hard disk built in a computersystem. Furthermore, the “computer-readable recording medium” mayinclude one that dynamically holds a program for a short time, such as acommunication line for transmitting a program via a network such as theInternet or a communication line such as a telephone line, and one thatholds a program for a certain period of time such as a volatile memoryinside a computer system serving as a server or a client in that case.Further, the program above may be for realizing a part of theabove-described functions, or may be one which can realize theabove-mentioned functions in combination with a program already recordedin the computer system.

The exemplary embodiments of the present invention have been describedabove in detail with reference to the figures. However, the specificconfiguration is not limited to these exemplary embodiments, and designsand the like not departing from the scope of the present invention areincluded.

INDUSTRIAL APPLICABILITY

The present invention can be applied to a social system that usesbiological pattern collation.

REFERENCE SYMBOLS

-   1, 5, 6 Biological pattern information processing device-   11 Biological pattern information acquisition unit-   61 Singular region detection unit-   62 Pre-registered biological pattern information memory unit-   70 Ridge line direction detection unit-   71 Ridge line pitch detection unit-   72 Ridge line intensity detection unit-   73 Direction singular point detection unit-   74 Abnormal pattern detection unit-   75 Comb type direction pattern detection unit-   76 ω type direction pattern detection unit-   77 X type direction pattern detection unit-   78 Ridge line breakage detection unit-   79 Cutout work detection unit-   91, 93 Damaged portion detection unit-   92 Z type surgery fingerprint restoration unit-   94 Damaged portion removal unit-   116 Collation unit-   214, 214 a Repair unit

1. A biological image information processing device comprising: amemory; and a hardware component that reads data from the memory and isconfigured to: acquire biological image information showing ridge lines;detect a singular region in a pattern of the ridge lines; determine, asa region for collation, a region free from the detected singular region;and perform collation of the pattern of the ridge lines using onlyfeature points that are included in the region for collation.
 2. Thebiological image information processing device according to claim 1,wherein the hardware component is configured to detect a position thatis furthest away from the singular region and configured to determinethe region free from the detected singular region based on the detectedposition.
 3. The biological image information processing deviceaccording to claim 2, wherein the hardware component is configured todetermine, as the region for collation, a region within which transitionof directions of the ridge lines from the detected position and pitchesof the ridge lines from the detected position are not abnormal.
 4. Thebiological image information processing device according to claim 2,wherein the hardware component is configured to determine, as the regionfor collation, a region of points included in a circle whose radius isequal to a length between the detected position and the abnormal region,the circle being centered at the detected position.
 5. The biologicalimage information processing device according to claim 2, wherein thehardware component is further configured to: when performing collationof the pattern of the ridge lines in which the singular region isdetected with a preregistered pattern, change at least either one of apositional deviation allowance which represents a degree to whichpositional deviation is allowed, and a mismatch allowance whichrepresents a degree to which a mismatch is allowed, such that thepattern of the ridge lines in which the singular region is detectedbecomes more likely to be regarded to be same as the pre-registeredpattern.
 6. A biological image processing method comprising: acquiringbiological image information showing ridge lines; detecting a singularregion in a pattern of the ridge lines; determining, as a region forcollation, a region free from the detected singular region; andperforming collation of the pattern of the ridge lines using onlyfeature points that are included in the region for collation.
 7. Thebiological image information processing method according to claim 6,further comprising detecting a position that is furthest away from thesingular region, wherein determining comprises determining the regionfree from the detected singular region based on the detected position.8. The biological image information processing method according to claim7, comprising determining, as the region for collation, a region withinwhich transition of directions of the ridge lines from the detectedposition and pitches of the ridge lines from the detected position arenot abnormal.
 9. The biological image information processing methodaccording to claim 7, comprising determining, as the region forcollation, a region of points included in a circle whose radius is equalto a length between the detected position and the abnormal region, thecircle being centered at the detected position.
 10. The biological imageinformation processing method according to claim 7, further comprisingchanging, when performing collation of the pattern of the ridge lines inwhich the singular region is detected with a pre-registered pattern, atleast either one of a positional deviation allowance which represents adegree to which positional deviation is allowed, and a mismatchallowance which represents a degree to which a mismatch is allowed, suchthat the pattern of the ridge lines in which the singular region isdetected becomes more likely to be regarded to be same as thepre-registered pattern.
 11. A non-transitory computer-readable storagemedium storing a program that causes a computer to perform: acquiringbiological image information showing ridge lines; detecting a singularregion in a pattern of the ridge lines; determining, as a region forcollation, a region free from the detected singular region; andperforming collation of the pattern of the ridge lines using onlyfeature points that are included in the region for collation.
 12. Thestorage medium according to according to claim 11, wherein the programfurther causes the computer to perform detecting a position that isfurthest away from the singular region, and wherein determiningcomprises determining the region free from the detected singular regionbased on the detected position.
 13. The storage medium according toclaim 12, wherein the program further causes the computer to performdetermining, as the region for collation, a region within whichtransition of directions of the ridge lines from the detected positionand pitches of the ridge lines from the detected position are notabnormal.
 14. The storage medium according to claim 12, wherein theprogram further causes the computer to perform determining, as theregion for collation, a region of points included in a circle whoseradius is equal to a length between the detected position and theabnormal region, the circle being centered at the detected position. 15.The storage medium according to claim 12, wherein the program furthercauses the computer to perform changing, when performing collation ofthe pattern of the ridge lines in which the singular region is detectedwith a pre-registered pattern, at least either one of a positionaldeviation allowance which represents a degree to which positionaldeviation is allowed, and a mismatch allowance which represents a degreeto which a mismatch is allowed, such that the pattern of the ridge linesin which the singular region is detected becomes more likely to beregarded to be same as the pre-registered pattern.